HTTP Adaptive Streaming Standards

• MPEG DASH Standard [1]
• 3GP DASH Standard [2]
• HbbTV DASH Recommendation [3]

A comparison in terms of data description format, video codec, audio codec, format, and segment length can be found in TABLE II of [4].

References
[1] Information Technology—Dynamic Adaptive Streaming Over HTTP (DASH)—Part 1: Media Presentation Description and Segment Formats, ISO/IEC 23009-1:2012, 2012.
[2] European Telecommunications Standard Institute (ETSI). (2009). Universal Mobile Telecommunication System (UMTS); LTE; Transparent end-to-end Packet-Switched Streaming Service (PSS); Protocols and Codecs, Sophia-Antipolis Cedex, France, 3GPP TS 26.234 Version 9.1.0 Release 9.
[3] HbbTV Specification, HbbTV Association, Erlangen, Germany, 2012.
[4] Seufert, Michael, et al. "A survey on quality of experience of http adaptive streaming." IEEE Communications Surveys & Tutorials 17.1 (2015): 469-492.

Adaptive Video Streaming Products

• Adobe HTTP Dynamic Streaming (HDS) [1]
• Apple HTTP Live Streaming (HLS) [2]
• Microsoft Live Smooth Streaming [3]
• Adaptive Scalable Video Streaming over HTTP [4]

For an experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP, refer to [5].

References

[1] Adobe Systems: HTTP Dynamic Streaming (HDS), February 2015, Available: http://tiny.cc/HDS.

[2] Apple: HTTP Live Streaming (HLS), February 2015, Available: http://tiny.cc/HLS2015.

[3] Microsoft: Live Smooth Streaming, February 2015, Available: http://tiny.cc/MSSmooth.
[4] S. Xiang. Scalable streaming. https://sites.google.com/site/svchttpstreaming/.
[5] S. Akhshabi, A. C. Begen, and C. Dovrolis. An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In ACM MMSys’11, pages 157–168, New York, NY, USA, 2011.

Encoding Settings For Streaming Vancouver Olympics

Level	Bitrate (kbps)	Resolution	Frame rate
1	400	312x176	15
2	600	400x224	15
3	900	512x288	15
4	950	544x304	15
5	1250	640x360	25
6	1600	736x416	25
7	1950	848x480	25
8	3450	1280x720	30

References
[1] Jan Ozer, “Adaptive Streaming in the Field”, in Streaming Media Magazine, 2011.
[2] Liu, Yao, et al. "User experience modeling for DASH video." 2013 20th International Packet Video Workshop. IEEE, 2013.

What affects the quality of adaptive video streams?

• Quality Switches
• Amplitude
• Frequency
• Stallings
• Duration 
• Frequency
• Initial Startup Delay

There are some surveys on the quality of experience for adaptive video streaming:

• Quality of Experience and HTTP adaptive streaming: A review of subjective studies [1]
• A Survey on Quality of Experience of HTTP Adaptive Streaming [2]
• User experience modeling for DASH video [3]

QoE is affected by spatial and temporal quality of the video stream. Initial delay, total stall duration, and number of stalls affect the temporal quality. Average video quality, number of switches, and average switch magnitude affect the spatial quality. In [3], the effect of each of these factors has been quantified.

Some important notes:

• Gradual multiple variations are preferred over abrupt variations.
• Constant quality is usually preferred to varying quality.
• In general, providing a bitrate as high as possible does not necessarily lead to the highest QoE.
• All is end that ends well: the end quality of the video has a definite impact on the perceived quality.
• The effect of spatial and temporal switching varies depending on the content type. 
• A single long stalling is preferred over multiple short freezes.
• Regular freezes are preferred over irregular freezes.
• Tolerable startup delay is dependent on the type of the application.
• Users prefer to wait longer if they can get less video stalling.       

Metrics

• Number of Quality Changes (NoC) [5]
• Number of Interruptions (NoI) [5]
• Percentage of Interruptions(PoI) [5]
• Impairment due to initial delay [3]
• Impairment due to stall [3]
• Impairment due to level fluctuations [3]
• Impairment due to low level video quality [3]
• Average playback Quality (APQ) [4]
• Playback Smoothness (PS) [6]
• Interruption Ratio [4]

References

[1] Garcia, M-N., et al. "Quality of experience and HTTP adaptive streaming: A review of subjective studies." Quality of Multimedia Experience (QoMEX), 2014 Sixth International Workshop on. IEEE, 2014.

[2] Seufert, Michael, et al. "A survey on quality of experience of http adaptive streaming." IEEE Communications Surveys & Tutorials 17.1 (2015): 469-492.
[3] Liu, Yao, et al. "User experience modeling for DASH video." 2013 20th International Packet Video Workshop. IEEE, 2013.
[4] S. Xiang, L. Cai, and J. Pan, “Adaptive scalable video streaming in wireless networks,” in Proc. of ACM MMSys, Feb. 2012, pp. 167–172.
[5] Yan, Zhisheng, Jingteng Xue, and Chang Wen Chen. "QoE continuum driven HTTP adaptive streaming over multi-client wireless networks." 2014 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2014.
[6] S. Nelakuditi, R. Harinath, E. Kusmierek, and Z. Zhang. Providing smoother quality layered video stream. In ACM NOSSDAV’00, June 2000.

DASHEncoder: DASH content generation tool

DASHEncoder is open source an available at: https://github.com/slederer/DASHEncoder


References
[1] Stefan Lederer, Christopher Müller and Christian Timmerer, “Dynamic Adaptive Streaming over HTTP Dataset”, In Proceedings of the ACM Multimedia Systems Conference 2012, Chapel Hill, North Carolina, February 22-24, 2012.

Comprehensive review of the MPEG DASH standard

A comprehensive review of the MPEG DASH standard for multimedia streaming over the internet can be found in [1].

References
[1] I. Sodagar, “The MPEG-DASH standard for multimedia streaming over the internet,” IEEE MultiMedia, vol. 18, no. 4, pp. 62–67, April 2011.

Adaptive Video Streaming Over LTE Networks

• Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network [1]
• The proposed algorithm can be used into two modes: client-side and network-assisted.
• Authors have considered three elements to assign the rates
• Client Buffer Size (DASH standard allows clients to report it)
• Channel Condition 
• Video Complexity
• Experiments have been done in "ns2".
• In their simulations, the available resources dedicated to streaming users is dynamically updated.
• They have used SSIM as for the quality metric.

• Optimization of HTTP adaptive streaming over mobile cellular networks [2]
• They have considered data and video users at the same time.
• They have used the same utility function for data and video users, however with different parameters.
• All fairness utility functions are of the same form [3].
• They didn't change the default proportionally fair scheduler inside eNB. They instructed that scheduler by providing it with GBR values. 
• They compared their work with Best Effort and traditional GBR schedulers. 
• All these schedulers share the same principle, described in [4].

References
[1] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
[2] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
[3] M. Uchida, J. Kurose, “An Information-Theoretic Characterization of Weighted α-Proportional Fairness,” In Proceedings of IEEE INFOCOM'09, ( pp. 1053-1061)
[4] M. Andrews, L. Qian, A. Stlyar, “Optimal Utility Based Multi-user Throughput Allocation subject to Throughput Constraints,” In Proceedings of IEEE INFOCOM'05, Vol. 4, pp. 2415-2424, 2005